The description of giant dipole resonance key parameters with multitask neural networks

Abstract

Giant dipole resonance (GDR) is one of the fundamental collective excitation modes in nucleus. Continuous efforts have been made to the evaluation of GDR key parameters in different nuclear data libraries. We introduced multitask learning (MTL) approach to learn and reproduce the evaluated experimental data of GDR key parameters, including both GDR energies and widths. Compared to the theoretical GDR parameters in RIPL-3 library, the accuracies of MTL approach are almost doubled for 129 nuclei with experimental data. The significant improvement is largely due to the right classification of unimodal nuclei and bimodal nuclei by the classification neural network. Based on the good performance of the neural network approach, an extrapolation to 79 nuclei around the $\beta$-stability line without experimental data is made, which provides an important reference to future experiments and data evaluations. The successful application of MTL approach in this work further proofs the feasibility of studying multi-output physical problems with multitask neural network in nuclear physics domain.

arXiv comment9 pages, 6 figures

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• Machine Learning in Nuclear Physics
• Multi-task learning on nuclear masses and separation energies with the kernel ridge regression
• Nuclear energy density functionals from machine learning
• Providing physics guides in Bayesian neural networks from input layer: case of giant dipole resonance predictions
• Nuclear mass predictions based on convolutional neural network
• Modeling Heavy-Ion Fusion Cross Section Data via a Novel Artificial Intelligence Approach
• Inference of Parameters for Back-shifted Fermi Gas Model using Feedback Neural Network
• Physics-informed interpretable forms for dipole resonances and quantities relevant for astrophysics